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Towards a Machine Learning Model for Predicting Failure of Agile Software Projects

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Ahmed Abdelaziz Mohamed, Nagy Ramadan Darwish, Hesham Ahmed Hefny
10.5120/ijca2017914466

Ahmed Abdelaziz Mohamed, Nagy Ramadan Darwish and Hesham Ahmed Hefny. Towards a Machine Learning Model for Predicting Failure of Agile Software Projects. International Journal of Computer Applications 168(6):20-26, June 2017. BibTeX

@article{10.5120/ijca2017914466,
	author = {Ahmed Abdelaziz Mohamed and Nagy Ramadan Darwish and Hesham Ahmed Hefny},
	title = {Towards a Machine Learning Model for Predicting Failure of Agile Software Projects},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2017},
	volume = {168},
	number = {6},
	month = {Jun},
	year = {2017},
	issn = {0975-8887},
	pages = {20-26},
	numpages = {7},
	url = {http://www.ijcaonline.org/archives/volume168/number6/27879-2017914466},
	doi = {10.5120/ijca2017914466},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Agile software development plays a very significant role in software projects. Agile software project is a refined approach to design and direct project processes. An agile project is finished in short sections called iterations. This paper introduces a survey of machine learning approaches for predicting failure of agile software projects. It reviews the uses of machine learning techniques such as fuzzy logic, multiple linear regression, neural network, logistic regression and etc., for predicting success and failure of agile software projects. This paper also proposes machine learning model for predicting failure of agile software projects. Many researches in this topic were reviewed, analyzed, summarized, and compared according to the used machine learning techniques in agile software projects.

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Keywords

Agile Software Projects, Machine Learning, Fuzzy Logic, Multiple Linear Regression.